Entropy and Clustering Information Applied to sEMG Classification

The EMG signal is very difficult to classify due to the stochastic complexity of its characteristics. A way to reduce the complexity of a signal is to use clusters to resize them to a smaller space and then perform the classification. A classification improvement was verified by clustering the electromyographic signal and comparing it with the possible movements that can be performed. In this study, the Agglomerative Hierarchical Clustering was used. The basic idea is to give prior information to the final classifier so the posterior classification has fewer classes, diminishing his complexity. Through the methodology applied in this article, an accuracy of more than 90% was achieved by using a time window of only 10 ms in a signal sampled at 2000 Hz. Experimentation confirms that the methods presented in this paper are competitive with other methods presented in the literature.